Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Esra Sivari, Guler Burcu Senirkentli, Erkan Bostanci, Mehmet Serdar Guzel, Koray Acici, Tunc Asuroglu

    Research output: Contribution to journalReview Articlepeer-review

    11 Citations (Scopus)
    23 Downloads (Pure)

    Abstract

    Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

    Original languageEnglish
    Article number2512
    Number of pages28
    JournalDiagnostics
    Volume13
    Issue number15
    DOIs
    Publication statusPublished - Jul 2023
    Publication typeA2 Review article in a scientific journal

    Keywords

    • convolutional neural network
    • deep learning
    • dental anomalies and diseases
    • dental diagnostics
    • dental images

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Clinical Biochemistry

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